Abstract: The range anxiety problem is prominent for pure electric commercial vehicles, and dynamically adjusts the energy recovery intensity according to changes in vehicle speed and acceleration during braking on complex roads. Verified by actual range tests under mountain road conditions of a commercial vehicle enterprise, the economy is improved by 4%, which confirms the effectiveness of the proposed method and provides technical support for improving the range of pure electric commercial vehicles. optimizing energy regeneration is key to improving the driving range. Energy regeneration relies on accurate acceleration information; However, complex road conditions and sensor noise can easily cause data distortion, leading to misjudgment of energy management strategies and reducing economy and safety. Therefore, an energy regeneration algorithm based on fused acceleration information is proposed for pure electric commercial vehicles. Firstly, considering the computing power constraints of the vehicle controller, an acceleration estimation algorithm based on Kalman filtering is proposed to correct and optimize the original sensor data to reduce errors. Secondly, an energy recovery algorithm fused with corrected acceleration information is constructed, which

Key words: Kalman filter; acceleration estimation; pure electric commercial vehicle; energy recovery; vehicle economy

摘要： 纯电动商用车里程焦虑问题突出，优化能量回收是提升续航的关键。能量回收依赖精 准的加速度信息，但复杂路况与传感器噪声易导致数据失真，造成能量管理策略误判，降低 经济性与安全性。为此提出一种适用于纯电动商用车的基于融合加速度信息的能量回收算法。 首先，结合整车控制器算力约束，提出基于卡尔曼滤波的加速度估计算法，对传感器原始数 据校正优化以降低误差；其次，构建融合校正后加速度信息的能量回收算法，在复杂运行道 路制动过程中根据车速、加速度变化动态调整能量回收强度。经某商用车企山区工况实际续 航试验，经济性提升 4%，验证了所提方法的有效性，为纯电动商用车续航提升提供了技术支 持。

关键词: 卡尔曼滤波；加速度估计；纯电动商用车；能量回收；汽车经济性